Improving HRI Through Robot Architecture Transparency

Abstract One ongoing challenge in human-robot interaction design is minimizing user misunderstandings and confusion. While engineers constantly improve the reliability of robots, the user’s mental model about robots and their limitations have to be addressed as well. In this work, we investigate ways to improve the human understanding about robots. For this, we propose FAMILIAR – FunctionAl user Mental model by Increased LegIbility ARchitecture , a transparent robot architecture with regard to the robot behavior and decision-making process. We conducted an exploratory online simulation user study (N=81) to evaluate two complementary approaches to convey and increase the knowledge about this architecture to non-expert users: a dynamic visualization of the system’s processes as well as an interface for defining the sequence of user and robot actions for teaching the robot, the interaction protocol. The experimental scenario consisted of teaching a robot about a simulated indoor environment. The results of this study reveal that the definition of an interaction protocol improves knowledge about the architecture measured via a questionnaire on knowledge of the different conceptual elements of the system (Sensors, Interaction Protocol, Behaviors, Preconditions, Actions, and how these interact: the Process). Furthermore, we show that with increased knowledge about the control architecture of the robot, users were significantly better in reaching the interaction goal. Moreover, we interestingly found that anthropomorphism may actually reduce interaction success. Our results support the crucial role of considering user mental models in robot architecture design.